Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Journal of Engineering Mechanics and Machinery
  4. Vol 10, Issue 1, 2025

Innovative Application of Gradient Descent to Optimize Strip Process Parameters

Download as PDF

DOI: 10.23977/jemm.2025.100105 | Downloads: 12 | Views: 506

Author(s)

Wenjing Wang 1

Affiliation(s)

1 College of Electrical and Control Engineering, Liaoning Technical University, Huludao, 125105, China

Corresponding Author

Wenjing Wang

ABSTRACT

Cold-rolled steel strip, renowned for its high strength, excellent toughness, and other superior properties, is extensively utilized across various industries. However, coupling parameters in the continuous annealing process poses significant challenges for quality control. To address this issue, this study employs the gradient descent algorithm to optimize the process parameters. By defining clear objectives, identifying key parameters, establishing a loss function, as well as iteratively updating the parameters, an optimal parameter combination is identified, thereby enhancing product quality and production efficiency. Experimental results demonstrate that the algorithm exhibits outstanding performance in optimizing hardness errors, with a notably low MSE value. Looking ahead, ‌research will focus on developing adaptive or real-time optimization systems to propel the intelligent development of the steel industry.

KEYWORDS

Coupling Parameters, Gradient Descent Algorithm, Iteratively Updating, MSE

CITE THIS PAPER

Wenjing Wang, Innovative Application of Gradient Descent to Optimize Strip Process Parameters. Journal of Engineering Mechanics and Machinery (2025) Vol. 10: 45-51. DOI: http://dx.doi.org/10.23977/jemm.2025.100105.

REFERENCES

[1] Cao Mengchuan, Wu Dan, Du Pengxuan. Comparative Analysis of Convergence Rates of Stochastic Gradient Descent and Batch Gradient Descent in Optimization of Lycium Barbarum Growth Model [J]. Modern Agricultural Machinery, 2024, (06): 75-77.
[2] Fang Su, Jia Jinwei, Yu Ling, et al. Optimal design method of wire bundle cross-section layout based on gradient descent continuous optimization algorithm [J]. Electric Power and Energy, 2024, 45 (05): 580-583+598.
[3] Liu Bing, Qiu Xu, Ding Yumin, et al. Research on Strip Production Process Optimization and Quality Control Based on Mathematical Model Analysis [J]. Journal of Liaoning University of Science and Technology, 2024, 26 (05): 13-17.
[4] Liu Jiaxu, Chen Song, Cai Shengze, et al. Design of Adaptive Fractional Gradient Optimization Algorithm Based on Robust Control [J]. Control Theory & Applications, 2024, 41 (07): 1187-1196.
[5] Chen Yan, Lei Xuejing, Yang Mei. Research on Optimal Valve Opening Scheme for Air Conditioning Control System Based on Gradient Descent [J]. China Machinery, 2024, (16): 8-12.
[6] Hao Zeyong. Research on collaborative optimization of process parameters of hot-rolled strip plate shape in multiple processes [D]. Yanshan University, 2024.
[7] Xing Haiyan, Yi Ming, Duan Chengkai, et al. Edge Recognition Model of Pipeline Defects Based on Magnetic Gradient Tensor Combined Invariant Algorithm Optimized by Improved Gradient Descent Algorithm [J]. China Mechanical Engineering, 2023, 34 (16): 1915-1920.
[8] Guo Zhuangzhuang. Research on Model Compression and Implicit Gradient Optimization Algorithm in Federated Meta-learning Framework [D]. Nanjing University of Science and Technology, 2023.
[9] Tian Zhiwei. Optimization of process parameters for cold-rolled galvanizing cleaning section [J]. Metallurgical Management, 2020, (19): 55-56. 
[10] Yang Jie, Hu Qi, Xiao Ting, et al. Energy Efficiency Modeling, Process Parameter Optimization and Ranking Modeling Optimization of Cold Rolling Continuous Retirement Unit Based on Energy Consumption [J]. China Mechanical Engineering, 2020, 31 (14): 1724-1732.

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.